Moonshot AI released Kimi K3 on July 16, its new flagship model, making it available immediately through Kimi.com, the Kimi app, Kimi Code and the Kimi API, with the full open-weight release to follow by July 27.

What shipped

Kimi K3 is a mixture-of-experts model with roughly 2.8 trillion total parameters, built on a new architecture Moonshot calls Kimi Delta Attention (KDA), a hybrid linear-attention mechanism, combined with a technique the company describes as Attention Residuals. The model supports a 1-million-token context window and native visual understanding, and Moonshot is pitching it specifically at long-horizon coding and agentic work: sustaining multi-step engineering tasks with minimal human supervision, navigating large codebases, and coordinating terminal tools. Two variants launched alongside the base model — K3 Max for chat and agent tasks, and K3 Swarm Max for large-scale parallel processing. On the API, Moonshot is pricing Kimi K3 at $0.30 per million input tokens with caching, $3.00 without, and $15.00 per million output tokens.

Where it stands

Moonshot's own benchmarking, as reported by MarkTechPost, says Kimi K3 delivers frontier-level results across its evaluation suite, consistently outperforming other open-weight models it was tested against, while still trailing the top proprietary systems on the market. The open-weight release is staggered: the model is live for use through Moonshot's hosted products and API now, but the downloadable weights, license and configuration files are not expected until July 27.

Why it matters

Kimi K3 is the latest in a run of large, open-weight releases from Chinese labs this year that trade off narrowly behind the leading proprietary models from OpenAI and Anthropic while remaining freely available to self-host and fine-tune — a dynamic that continues to pressure Western labs' pricing and closed-weight strategies. The model's explicit framing around long-horizon coding and agentic workflows, rather than general chat, also underscores how frontier labs are now optimizing flagship releases specifically for autonomous agent use cases instead of treating agentic capability as a secondary feature.